Rapid identification of mosquito species and age by mass spectrometric analysis.

BACKGROUND: A rapid, accurate method to identify and to age-grade mosquito populations would be a major advance in predicting the risk of pathogen transmission and evaluating the public health impact of vector control interventions. Whilst other spectrometric or transcriptomic methods show promise, current approaches rely on challenging morphological techniques or simple binary classifications that cannot identify the subset of the population old enough to be infectious. In this study, the ability of rapid evaporative ionisation mass spectrometry (REIMS) to identify the species and age of mosquitoes reared in the laboratory and derived from the wild was investigated. RESULTS: The accuracy of REIMS in identifying morphologically identical species of the Anopheles gambiae complex exceeded 97% using principal component/linear discriminant analysis (PC-LDA) and 84% based on random forest analysis. Age separation into 3 different age categories (1 day, 5-6 days, 14-15 days) was achieved with 99% (PC-LDA) and 91% (random forest) accuracy. When tested on wild mosquitoes from the UK, REIMS data could determine the species and age of the specimens with accuracies of 91 and 90% respectively. CONCLUSIONS: The accuracy of REIMS to resolve the species and age of Anopheles mosquitoes is comparable to that achieved by infrared spectroscopy approaches. The processing time and ease of use represent significant advantages over current, dissection-based methods. Importantly, the accuracy was maintained when using wild mosquitoes reared under differing environmental conditions, and when mosquitoes were stored frozen or desiccated. This high throughput approach thus has potential to conduct rapid, real-time monitoring of vector populations, providing entomological evidence of the impact of alternative interventions.

The intelligent knife (iKnife) and its intraoperative diagnostic advantage for the treatment of cervical disease.

Clearance of surgical margins in cervical cancer prevents the need for adjuvant chemoradiation and allows fertility preservation. In this study, we determined the capacity of the rapid evaporative ionization mass spectrometry (REIMS), also known as intelligent knife (iKnife), to discriminate between healthy, preinvasive, and invasive cervical tissue. Cervical tissue samples were collected from women with healthy, human papilloma virus (HPV) ± cervical intraepithelial neoplasia (CIN), or cervical cancer. A handheld diathermy device generated surgical aerosol, which was transferred into a mass spectrometer for subsequent chemical analysis. Combination of principal component and linear discriminant analysis and least absolute shrinkage and selection operator was employed to study the spectral differences between groups. Significance of discriminatory m/z features was tested using univariate statistics and tandem MS performed to elucidate the structure of the significant peaks allowing separation of the two classes. We analyzed 87 samples (normal = 16, HPV ± CIN = 50, cancer = 21 patients). The iKnife discriminated with 100% accuracy normal (100%) vs. HPV ± CIN (100%) vs. cancer (100%) when compared to histology as the gold standard. When comparing normal vs. cancer samples, the accuracy was 100% with a sensitivity of 100% (95% CI 83.9 to 100) and specificity 100% (79.4 to 100). Univariate analysis revealed significant MS peaks in the cancer-to-normal separation belonging to various classes of complex lipids. The iKnife discriminates healthy from premalignant and invasive cervical lesions with high accuracy and can improve oncological outcomes and fertility preservation of women treated surgically for cervical cancer. Larger in vivo research cohorts are required to validate these findings.

[Authenticity discrimination of Pulsatillae Radix based on dry-process REIMS fingerprinting combined with machine learning].

In this study, rapid evaporative ionization mass spectrometry(REIMS) fingerprints of 388 samples of roots of Pulsatilla chinensis(PC) and its common counterfeits, roots of P. cernua and roots of Anemone tomentosa were analyzed based on REIMS combined with machine learning. The samples were determined by REIMS through dry burning, and the REIMS data underwent cluster analysis, similarity analysis(SA), and principal component analysis(PCA). After dimensionality reduction by PCA, the data were analyzed by similarity analysis and self-organizating map(SOM), followed by modeling. The results indicated that the REIMS fingerprints of the samples showed the characteristics of variety differences and the SOM model could accurately distinguish PC, P. cernua, and A. tomentosa. REIMS combined with machine learning algorithm has a broad application prospect in the field of traditional Chinese medicine.

Advances in ionisation techniques for mass spectrometry-based omics research.

Omics analysis by mass spectrometry (MS) is a vast field, with proteomics, metabolomics and lipidomics dominating recent research by exploiting biological MS ionisation techniques. Traditional MS ionisation techniques such as electrospray ionisation have limitations in analyte-specific sensitivity, modes of sampling and throughput, leading to many researchers investigating new ionisation methods for omics research. In this review, we examine the current landscape of these new ionisation techniques, divided into the three groups of (electro)spray-based, laser-based and other miscellaneous ionisation techniques. Due to the wide range of new developments, this review can only provide a starting point for further reading on each ionisation technique, as each have unique benefits, often for specialised applications, which promise beneficial results for different areas in the omics world.

Rapid Evaporative Ionization Mass Spectrometry-Based Lipidomics for Identification of Canine Mammary Pathology.

The present work proposes the use of a fast analytical platform for the mass spectrometric (MS) profiling of canine mammary tissues in their native form for the building of a predictive statistical model. The latter could be used as a novel diagnostic tool for the real-time identification of different cellular alterations in order to improve tissue resection during veterinary surgery, as previously validated in human oncology. Specifically, Rapid Evaporative Ionization Mass Spectrometry (REIMS) coupled with surgical electrocautery (intelligent knife-iKnife) was used to collect MS data from histologically processed mammary samples, classified into healthy, hyperplastic/dysplastic, mastitis and tumors. Differences in the lipid composition enabled tissue discrimination with an accuracy greater than 90%. The recognition capability of REIMS was tested on unknown mammary samples, and all of them were correctly identified with a correctness score of 98-100%. Triglyceride identification was increased in healthy mammary tissues, while the abundance of phospholipids was observed in altered tissues, reflecting morpho-functional changes in cell membranes, and oxidized species were also tentatively identified as discriminant features. The obtained lipidomic profiles represented unique fingerprints of the samples, suggesting that the iKnife technique is capable of differentiating mammary tissues following chemical changes in cellular metabolism.

Evaluating the ability of rapid evaporative ionization mass spectrometry to differentiate beef palatability based on consumer preference.

Rapid Evaporative Ionization Mass Spectrometry (REIMS) is a type of ambient ionization mass spectrometry, which enables real-time evaluation of several complex traits from a single measurement. The objective of this study was to evaluate the capability of REIMS analysis of raw samples coupled with chemometrics to accurately identify and predict cooked beef palatability. REIMS analysis and consumer sensory evaluation were conducted for beef arm center roasts (n = 20), top loin steaks (n = 20), top sirloin steaks (n = 20), and 20% lipid ground beef (n = 20). These data were used to train predictive models for six classification sets representing different sensory traits. The maximum prediction accuracies achieved (from high to low): beefy flavor acceptance (86.25%), juiciness acceptance (83.75%), overall acceptance (81.25%), overall flavor acceptance (81.25%), grilled flavor acceptance (78.75%), and tenderness acceptance (75%). The current study demonstrates that REIMS analysis of raw meat has the potential to predict and classify cooked beef palatability. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05562-6.

Lipidomic analysis and diagnosis of glioblastoma multiforme with rapid evaporative ionization mass spectrometry.

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor in the central nervous system. GBM patients have a very low 5-year survival rate and most of them died within 1 year. Conventional histopathological examination for GBM diagnosis is complicated and time-consuming, which always blocks the development of more precise and effective treatments in resection operation. Rapid evaporative ionization mass spectrometry (REIMS) is a MS technique in clinical medicine research, which combines the common diathermy device with MS to acquire the lipid profiles of tissue specimens for lipidomic analysis and real-time tumor diagnosis. In this study, the REIMS method employing bipolar forceps was optimized and validated for high-throughput lipidomics and diagnosis of GBM for the first time. Total 42 lipid metabolites were tentatively identified and 12 out of 13 lipid biomarkers showed higher intensities in GBM, which were consistent with previous studies. After this, a statistic model was built with the lipidomic data for the diagnosis of GBM tumor in real-time. The diagnostic accuracy (94.74%), sensitivity (95.38%), and specificity (93.33%) were evaluated with histopathology validated brain tissue specimens that were not used in the training set. The proposed REIMS method for the lipidomic-analysis and diagnosis of GBM tumor provides a new direction for MS-based lipidomics and precision medicine and might be used to guide surgeons to precisely resect the GBM tissue and keep the normal brain tissue in operation.

Exploration of Rapid Evaporative-Ionization Mass Spectrometry as a Shotgun Approach for the Comprehensive Characterization of Kigelia Africana (Lam) Benth. Fruit.

Rapid evaporative-ionization mass spectrometry (REIMS) coupled with an electroknife as a sampling device was recently employed in many application fields to obtain a rapid characterization of different samples without any need for extraction or cleanup procedures. In the present research, REIMS was used to obtain a metabolic profiling of the Kigelia africana fruit, thus extending the applicability of such a technique to the investigation of phytochemical constituents. In particular, the advantages of REIMS linked to a typical electrosurgical handpiece were applied for a comprehensive screening of this botanical species, by exploiting the mass accuracy and tandem MS capabilities of a quadrupole-time of flight analyzer. Then, 78 biomolecules were positively identified, including phenols, fatty acids and phospholipids. In the last decade, Kigelia africana (Lam.) Benth. fruit has attracted special interest for its drug-like properties, e.g., its use for infertility treatments and as anti-tumor agent, as well as against fungal and bacterial infections, diabetes, and inflammatory processes. Many of these properties are currently correlated to the presence of phenolic compounds, also detected in the present study, while the native lipid composition is here reported for the first time and could open new directions in the evaluation of therapeutic activity.

Tissue classification by rapid evaporative ionization mass spectrometry (REIMS): comparison between a diathermic knife and CO2 laser sampling on classification performance.

The increasing need for rapid, in situ, and robust tissue profiling approaches in the context of intraoperative diagnostics has led to the development of a large number of ambient ionization-based surface sampling strategies. This paper compares the performances of a diathermic knife and a CO2 laser handpiece, both clinically approved, coupled to a rapid evaporative ionization mass spectrometry (REIMS) source for quasi-instantaneous tissue classification. Several fresh meat samples (muscle, liver, bone, bone marrow, cartilage, skin, fat) were obtained from different animals. Overall, the laser produced cleaner cuts and more reproducible and higher spectral quality signals when compared with the diathermic knife (CV laser = 9-12%, CV diathermic = 14-23%). The molecular profiles were subsequently entered into a database and PCA/LDA classification/prediction models were built to assess if the data generated with one sampling modality can be employed to classify the data generated with the other handpiece. We demonstrate that the correct classification rate of the models increases (+ 25%) with the introduction of a model based on peak lists that are tissue-specific and common to the two handpieces, compared with considering solely the whole molecular profile. This renders it possible to use a unique and universal database for quasi-instantaneous tissue recognition which would provide similar classification results independent of the handpiece used. Furthermore, the laser was able to generate aerosols rich in lipids from hard tissues such as bone, bone marrow, and cartilage. Combined, these results demonstrate that REIMS is a valuable and versatile tool for instantaneous identification/classification of hard tissue and coupling to different aerosol-generating handpieces expands its field of application. Graphical abstract.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery.

BACKGROUND: Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment. METHODS: Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos. RESULTS: A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40). CONCLUSIONS: The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Meet interesting abbreviations in clinical mass spectrometry: from compound classification by REIMS to multimodal and mass spectrometry imaging (MSI).

This feature article discusses two modern mass spectrometry abbreviations in their clinical applications. Rapid evaporative ionization mass spectrometry (REIMS) is reported as a molecular classification tool useful for spectral features definition prior to mass spectrometry imaging (MSI). REIMS is appreciated not only as an ionization technique coupled with a surgical device but particularly as a biomarker discovery tool. For more complex understanding of pathological processes at cellular and molecular levels, the importance of multimodal approach in imaging applications is documented in the context of fiducial markers needed for hyperspectral data fusion collected by optical microscopy, elemental and molecular MSI. Finally, pathogen inactivation needed prior to the sectioning of the infected tissue is reported, and the impact of formaldehyde crosslinking to signal reduction is discussed.

Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics.

INTRODUCTION: The last 20 years have seen significant improvements in the analytical capabilities of biological mass spectrometry (MS). Studies using advanced MS have resulted in new insights into cell biology and the etiology of diseases as well as its use in clinical applications. AREAS COVERED: This review discusses recent developments in MS-based technologies and their cancer-related applications with a focus on proteomics. It also discusses the issues around translating the research findings to the clinic and provides an outline of where the field is moving. Expert commentary: Proteomics has been problematic to adapt for the clinical setting. However, MS-based techniques continue to demonstrate potential in novel clinical uses beyond classical cancer proteomics.

Discrimination of beef composition and sensory quality by using rapid Evaporative Ionisation Mass Spectrometry (REIMS).

Herein, we investigated the potential of REIMS analysis for classifying muscle composition and meat sensory quality. The study utilized 116 samples from 29 crossbred Angus × Salers, across three muscle types. Prediction models were developed combining REIMS fingerprints and meat quality metrics. Varying efficacy was observed across REIMS discriminations - muscle type (71 %), marbling level (32 %), untrained consumer evaluated tenderness (36 %), flavor liking (99 %) and juiciness (99 %). Notably, REIMS demonstrated the ability to classify 116 beef across four Meat Standards Australia grades with an overall accuracy of 37 %. Specifically, "premium" beef could be differentiated from "unsatisfactory", "good everyday" and "better than everyday" grades with accuracies of 99 %, 84 %, and 62 %, respectively. Limited efficacy was observed however, in classifying trained panel evaluated sensory quality and fatty acid composition. Additionally, key predictive features were tentatively identified from the REIMS fingerprints primarily comprised of molecular ions present in lipids, phospholipids, and amino acids.

Rapid LA-REIMS-based metabolic fingerprinting of serum discriminates aflatoxin-exposed from non-exposed pregnant women: a prospective cohort from the Butajira Nutrition, Mental Health, and Pregnancy (BUNMAP) Study in rural Ethiopia.

To date, the changes in maternal metabolic response associated with prenatal aflatoxin exposure remain largely unknown. This study investigated the effects of prenatal aflatoxin exposure on the maternal serum metabolome in rural Ethiopia. A total of 309 pregnant women were enrolled prospectively, and their serum aflatoxin concentrations were measured using targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Serum metabolic fingerprints were obtained using laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS), followed by combination of univariate and multivariate statistical modelling to evaluate changes in circulating metabolic features between aflatoxin-exposed and unexposed mothers and to select discriminatory metabolic features. The analysis revealed that 81.8% of women were exposed to aflatoxins, with a median concentration of 12.9 pg/mg albumin. The orthogonal partial least square discriminant analysis (OPLS-DA) regression model demonstrated significant disparities in the serum metabolome when comparing Ethiopian pregnant women with low vs high aflatoxin exposure. Thirty-two differentially expressed metabolic features were identified, affecting aminoacyl-tRNA biosynthesis pathway. Several discriminatory metabolites have been identified, including glutamine, tryptophan, tyrosine, carnosine, and 1-methylnicotinamide. In conclusion, our findings indicate that aflatoxin exposure during pregnancy have shown disparities in the maternal serum metabolome, primarily affecting protein synthesis. Further research is needed to identify specific metabolite biomarkers and elucidate the underlying mechanisms.

Evaluation of Lipid Oxidation Characteristics in Salmon after Simulation of Cold Chain Interruption Using Rapid Evaporation Ionization Mass Spectrometry.

Temperature fluctuations occurring during the cold chain logistics of salmon contribute to lipid oxidation. This study aimed to simulate cold chain interruption through freeze-thaw operations and evaluate the lipidomics data from salmon samples subjected to different numbers of freeze-thaw cycles by using rapid evaporative ionization mass spectrometry (REIMS) combined with an intelligent surgical knife (iKnife). The results indicated significant differences in the relative abundance of characteristic ions among the samples (p < 0.05). A total of 34 ions with variable importance for the projection values ≥1 were identified as potential biomarkers, including m/z 719.4233 ([PCC36:5-NH(CH3)3]-), m/z 337.3134 ([FAC22:1]-), m/z 720.4666 ([PEC35:6-H]-), m/z 309.2780 ([FAC20:1]-), m/z 777.4985 ([PCC40:4-NH(CH3)3]-), m/z 745.4421 ([PCC38:6-NH(CH3)3]-/[PEC38:6-NH3]-), m/z 747.4665 ([PCC38:5-NH(CH3)3]-/[PEC38:5-NH3]-), etc. The degree of lipid oxidation was found to be associated with the number of freeze-thaw cycles, exhibiting the most significant alterations in the relative abundance of lipid ions in the 8T samples. Additionally, sensory evaluation by the CIE-L*a*b* method and volatile analysis by headspace solid-phase microextraction gas chromatography-mass spectrometry demonstrated significant differences (p < 0.05) in color and odor among the salmon samples, with a correlation to the number of freeze-thaw cycles. The primary compounds responsible for alterations in salmon odor were aldehydes with lower odor thresholds. In summary, the iKnife-REIMS method accurately differentiated salmon muscle tissues based on varying levels of lipid oxidation, thus expanding the application of REIMS.

An in situ and real-time analytical method for detection of freeze-thew cycles in tuna via IKnife rapid evaporative ionization mass spectrometry.

Freezing is one of the most commonly used preservation methods for Bluefin tuna (Thunnus orientalis). However, repeated freezing and thawing would inevitably occur due to the temperature fluctuation, leading to the microstructure damage, lipid oxidation and protein integrity decline of tuna muscle without notable visual appearance change. In this study, we used a rapid evaporative ionization mass spectrometry (REIMS) technique for the real-time determination of the extent of repeated freezing and thawing cycles in tuna fillets. We found significant variance in the relative abundance of fatty acids between bluefin tuna and its fresh counterpart following freeze-thaw cycles. Meanwhile, the difference is statistically significant (p < 0.05). The quality of tuna remains largely unaffected by a single freeze-thaw cycle but significantly deteriorates after freeze-thaw cycles (freeze-thaw count ≥2), and the relative fatty acid content of the ionized aerosol analysis in the REIMS system positively correlated with the number of freeze-thaw cycles. Notably, palmitic acid (C 16:0, m/z 255.23), oleic acid (C 18:1, m/z 281.24), and docosahexaenoic acid (C 22:6, m/z 327.23) displayed the most pronounced changes within the spectrum of fatty acid groups.

High-Throughput Drug Stability Assessment via Biomimetic Metalloporphyrin-Catalyzed Reactions Using Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry (LA-REIMS).

Background: Building extensive drug candidate libraries as early in the development pipeline as possible, with high-throughput in vitro absorption, distribution, metabolism, and excretion (ADME) profiling, is crucial for the selection of lead compounds to guide subsequent research and production phases. Traditionally, the analysis of metabolic stability assays heavily relies on high-throughput LC-MS/MS (liquid chromatography tandem mass spectrometry) techniques to meet with the lead profiling demands. Laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) is a quick and efficient technique for characterizing complex biological samples without laborious sample preparation. Objective: In this study, using an automated LA-REIMS well plate reader, achieving an 8 s per sample measurement time, the oxidative metabolic stability of active drug agents was assessed using biomimetic metalloporphyrin-based oxidative model reactions. Results: The results obtained using the novel LA-REIMS-based protocol were compared to and corroborated by those obtained using conventional HPLC-UV-MS (high performance liquid chromatography with ultra-violet detection coupled with mass spectrometry) measurements. Conclusions: LA-REIMS emerges as a promising technique, demonstrating potential suitability for semi-quantitative high-throughput metabolic stability in an optimized solvent environment.

New Insights into Phospholipid Profile Alteration of Bigeye Tuna (Thunnus obesus) during Daily Cooking Processes Using Rapid Evaporative Ionization Mass Spectrometry.

Bigeye tuna (BET, Thunnus obesus) is one of the most nutritious and luxurious cosmopolitan fish. The cooked BET products are capturing the interests of consumers by enhancing flavor and ensuring microbiological safety; however, the lipidomic fingerprints during daily cooking processes have not been investigated. In this work, lipid phenotypic data variation in BET during air-frying, roasting, and boiling was studied comprehensively using iKnife rapid evaporative ionization mass spectrometry (REIMS). The outstanding lipid ions mainly including fatty acids (FAs) and phospholipids (PLs) were identified structurally. It was demonstrated that the rates of heat transfer and lipid oxidation in air-fried BET were slower than those in roasted and boiled BET by elucidating the lipid oxidation and PL hydrolysis mechanism. Furthermore, multivariate REIMS data analysis (e.g., discriminant analysis, support vector machine, neutral network, and machine learning models) was used to characterize the lipid profile change in different cooked BET samples, among which FAC22:6, PL18:3/22:6, PL18:1/22:6, and others were the salient contributing features for determining the cooked BET samples. These results may provide a potential strategy for a healthy diet by controlling and improving functional food quality in daily cooking.

Rapid Evaporative Ionisation Mass Spectrometry fingerprinting can discriminate lamb meat due to different ageing methods and levels of dehydration.

Dry-ageing is a technique for developing characteristic dry-aged flavour through the interplay of dehydration, lipid oxidation and microbial activities. In this study, we tested the hypothesis that the extent of drying influences the metabolite profile and the final flavour of lamb using an "Age-and-Dry" regime; and that Rapid Evaporative Ionisation Mass Spectrometry (REIMS) fingerprinting can be used to discriminate the metabolic fingerprints of lambs due to ageing methods and dehydration levels. Lamb loins (n = 60) were dehydrated with low (12%), medium (17%) and high (22%) weight losses and compared with the wet-aged equivalents using REIMS and evaluated by 12-member sensory panel. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models based on 1400 tentatively identified m/z features were obtained for ageing methods (Q2 > 0.95) and dehydration levels (Q2 > 0.82) with high discrimination accuracy. Increased concentrations of dipeptides and metabolites associated with energy metabolism were observed in aged-and-dried lamb meat which supports the umami and savoury taste perceived by the sensory panel. A reduced concentration of polyunsaturated fatty acids with more aldehydes was observed in aged-and-dried lamb meat contributing to the nutty, roasted, and fatty flavour notes detected by the sensory panellists in these samples compared to the wet-aged. SIGNIFICANCE: Outcomes of this study demonstrated that REIMS can be used to accurately discriminate between different ageing methods and dehydration levels of meat, and the compounds that are associated with lamb flavour. The implications of this finding include: (1) the metabolite concentrating effect of dehydration and the associated effect on aged lamb flavour determined in this study could be used to tailor the processing of dry-aged lamb to deliver specific flavour outcomes in an industrial setting; (2) the ability of REIMS to rapidly detect differences in the aged lamb flavour due to dehydration effect would improve the quality of dry-aged meat and the efficiency with which it can be industrially produced. Thus, REIMS can be used as a rapid authentication and quality prediction tool for different ageing regimes and flavour for the meat industry.

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification.

The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.